1. Field of the Invention
This invention relates to the field of computing and methods to model business.
2. Background
Large business enterprises have many complex and interrelated business processes. Changes in one process many have many other unintended effects on other processes. Business analysts often have to figure out or problem solve on these processes when something is detected to have gone wrong or to improve and change the business. These analysts use many reports to understand how the business is functioning and often will create many other ad-hoc reports to determine exactly how to respond to problems or how to change the business. A large business many have many thousands of standard reports and have many thousands more of ad-hoc created reports. Few of those ad-hoc reports are ever used again because the context and reason for the report are not understood by anyone else and thus cannot be located. This leads to the same or very similar ad-hoc reports being created many times.
Few people in a single business understand more than a dozen or so reports and this makes it very difficult to determine what is actually causing problems in the business. Finding the root causes of the problem may be difficult and often there are only a few people who understand enough about how the business operates, how and where to find the data to explore, and can apply enough knowledge to correct the problem. These people have knowledge that is very important to the business and this invention helps capture that knowledge and let others utilize it by visually showing the relationships of business processes, the relevant measures of those processes, and cause and effect chains.
This invention uses causal models, business process models, and dimensional report concepts to capture the knowledge that experienced business analysts have when they problem solve on a business. FIG. 14 shows the process that business analysts use to do that problem solving. This invention directly aids in three of the boxes depicted in the problem solving flow; 1) determine root causes, 2) determine possible actions, and 3) monitor results and effects.
A causal model shows variables and factors and the influence they have on others. For example, the costs of health care is influenced by the number of people who smoke. Changes in the number of people who smoke will change the costs of future health care, thus the 2 variables are linked by a causal arc. See FIG. 1 for an example of how more complex models can be visually represented. The causal links may have the direction of the effect shown on the link, for example, an increase in the number of smokers gives an increase in the health costs. This could be shown by using a plus sign icon. The reverse relationship could be shown using a minus sign icon. The link may have other properties that may be qualitative or quantitative, for example, an expected time delay. The time between an increase in smokers and an increase in costs could be several years. This can be shown by using a italic letter D icon.
Causal models have various other names and uses and somewhat different formalisms to support a particular use. They are very similar to Influence Diagrams, Causal Loops, and System Dynamics. See Coyle.
Business process models generally fall into activity/process models or Petri-net models which are equivalent but are often notated differently. The process model is the more commonly encountered type and most available tools for process modeling handle that type. They can be referred to as activity models or business process models or workflow models. We will refer to them as activity models. The activity models show the sequence of actions needed for a process along with the resources consumed, used and created along with major decision points. An example visual representation of a process model is shown in FIG. 16. Process models can be modeled as a type of activity diagram in UML. See OMG UML specification.
Dimensional reports are reports that show metrics broken down by dimension elements. An example report is shown in FIG. 13. See Thompsen, OLAP Solutions. Metrics, or measures, are the numbers in the cells of the report, for example the number of smokers or the sales of a particular product or product family. The dimensions are the ways the measures are broken out. Each dimension has elements that partition that dimension. For example, a geographic dimension is partitioned by US states as elements. Furthermore, that dimension may have a hierarchy of element partitions. That geographic dimension may have regions which consist of multiple states. The report can then for example give totals for each state and also for each region. Most reports are dimensional in nature, however, they do not have to show more than one level of a dimension. The power of a dimensional report comes from having hierarchies that can be used to examine how different elements and levels affect the measures. A single report can show multiple different measures on the common dimensions, for example, both health care costs and number of smokers can be on a single report and be broken down by region and state.
This invention combines each of the models in a novel way to allow navigation between the causal models, the process models and the reports while incorporating knowledge of prior problem solving experience.
Problems in a business may be indicated by reports that show measures deviating from a desired value or expected behavior. These measures are the metrics of activities or processes of the business and the measures can be directly related to the business process models for the activity or process they correspond to. The business process model can show how those measures are related to other measures in the system. Most often, the measure that is used to alert to business analyst is not directly changeable. For example, a report showing sales have dropped does give the reason why and what should be done. Understanding the root causes of a problem requires a theory of cause and effect. To take action to address that problem also requires a theory of cause and effect that must take into account that the action may directly affect the root cause of the problem or just compensate for it.
The causal models captures a portion of that cause and effect theory. They show those measures and other measures and factors that are important in understanding the mechanism of the effect. Some factors in the model may not correspond to variables that are not directly measurable and thus not in a report, but are still very useful in understanding why the effect connection is true. The causal model can also indicate what variables and factors are controllable by the business, for example adjusting the price. These are called levers. The variables on the causal model can be directly linked to and thus navigable to reports that give information on that variable and the current environment. In addition, unstructured knowledge, such as Word documents or PDF files or GOOGLE searches can be linked to the variables in the causal model. Those variables can also be directly connected and link to the business process models if they are measures of a modeled process. The can link to the actual activity metric on the model. If the causal model has accurate information on quantitative effects of the causal links, the model can be quantitatively simulated to check for effect ranges as in standard Systems Dynamics analysis.
The creation of causal models is difficult and complex because they are usually derived from experience. Business analysts take a long time to understand the dynamics of a business and how to gather the information needed to gain the correct insight. It is that experience that is so important to capture. The building and maintaining of the causal models is an on-going endeavor where new factors are discovered and new hypotheses are created in a continuous process. By capturing the knowledge in a formal interrelated model, the knowledge can be shared and reused by other people.